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• W1531492092 abstract "The growth in interest in RGB-D devices (e.g. Microsoft Kinect or ASUS Xtion Pro) is based on their low price, as well as the wide range of possible applications. These devices can provide skeletal data consisting of 3D position, as well as orientation data, which can be further used for pose or action recognition. Data for 15 or 20 joints can be retrieved, depending on the libraries used. Recently, many datasets have been made available which allow the comparison of different action recognition approaches for diverse applications (e.g. gaming, Ambient-Assisted Living, etc.). In this work, a genetic algorithm is used to determine the contribution of each of the skeleton's joints to the accuracy of an action recognition algorithm, thus using or ignoring the data from each joint depending on its relevance. The proposed method has been validated using a k-means-based action recognition approach and using the MSR-Action3D dataset for test. Results show the presented algorithm is able to improve the recognition rates while reducing the feature size." @default.
• W1531492092 created "2016-06-24" @default.
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• W1531492092 date "2013-01-01" @default.
• W1531492092 modified "2023-09-25" @default.
• W1531492092 title "Optimal Joint Selection for Skeletal Data from RGB-D Devices Using a Genetic Algorithm" @default.
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• W1531492092 doi "https://doi.org/10.1007/978-3-642-37798-3_15" @default.
• W1531492092 hasPublicationYear "2013" @default.
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